Type I error description?

Type I error description?

• A. It is rejecting a true null hypothesis
• B. It is failing to reject a true null hypothesis
• C. It is rejecting a false null hypothesis
• D. It is failing to reject a false null hypothesis

Answer: (A)

Rejecting a true null hypothesis is what a Type I error describes. In hypothesis testing, the null hypothesis typically states that there is no effect or no difference. If the data lead you to conclude that there is an effect or difference when in reality there isn’t one, you’ve committed a Type I error. The probability of making this error is the significance level, alpha, often set at 0.05. This is essentially a false positive result. By contrast, failing to reject a true null is a correct decision; rejecting a false null is a desirable outcome reflecting the test’s power; and failing to reject a false null is a Type II error (a false negative).

Rejecting a true null hypothesis is what a Type I error describes. In hypothesis testing, the null hypothesis typically states that there is no effect or no difference. If the data lead you to conclude that there is an effect or difference when in reality there isn’t one, you’ve committed a Type I error. The probability of making this error is the significance level, alpha, often set at 0.05. This is essentially a false positive result. By contrast, failing to reject a true null is a correct decision; rejecting a false null is a desirable outcome reflecting the test’s power; and failing to reject a false null is a Type II error (a false negative).